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Carmona-Duarte et al. Keywords Sigma-lognormal Parkinson ALS ⋅ ⋅ Formant ⋅ Kinematic ⋅ Articulation ⋅ 1 Introduction The Kinematic Theory of rapid human movements [1] has successfully been applied to handwriting modeling. According to this theory, the way in which neuromuscular systems are involved in the production of muscular movements is modelled using lognormal velocity proﬁles, here after referred to as the Sigma-lognormal model [1]. This theory has been successfully used in handwriting, studying the neuromuscular system for rapid movements [2], the handwriting variations in time [3, 4], the speciﬁcation of a diagnostic system for neuromuscular disorder [5], the assessment of brain stroke risk factors [6], etc.

3. It may be concluded that the mood series of participant 7 is quite unlike that of the other participants. Furthermore the dendogram provides insight into how the participants are clustered and provides a quick view into how a participant compares to others participants. An example of two 6 Speciﬁcally referred as UPGMA. Exploring and Comparing Machine Learning Approaches . . 45 Fig. 4 Similarity DTW participants 16 and 24 similar time series and the resulting DWT path is illustrated in Fig.

Van Breda et al. high data Range [0, 1] [0, 1] [0, 1] [0, 1] [0, 1] [0, 1] [0, 1] [0, 1] [0, 1] [0, 1] [0, 1] [0, 1] [0, 1] Most of the where T is the target feature, OF is an obtrusive feature, and UF is an unobtrusive feature 3 Method In this section we describe the methodology we have followed for generating our predictive models. We begin by discussing the steps taken to prepare the data: the process of derivation of additional attributes on top of the attributes described in Sect. 2 and the imputation of missing values.